In recent years, many theoretical SR system design tools have become available
 [1]}, [2]}, [3]}, [4]}. These tools have made it
easier to conduct mathematical analysis of algorithms and derive analytical, rather
than weakly inductive proofs of correctness [5]}. Despite this, there
has not been a corresponding increase in the average swarm sizes used to evaluate new
algorithms for tackling problems of real-world scale (notable exceptions
include [3]} (600 robots), [7]} (768
robots), [8]} (375 robots), [9]} (16,384)). Investigation
of simple behaviors such as pattern formation, localization, or collective motion,
where design and computational complexity do not inherently limit scalability, is
generally evaluated with relatively small scales
([10]} (20 robots),
 [5]} (40 robots)). Methods for more complex behaviors are likewise
tested at similar scales: foraging [12]}, [13]} (20
robots), [14]} (30 robots), task allocation [2]} (25
robots). While such sizes technically meet Sahin's criteria, they fall short of their
biological origins (i.e. thousands, if not millions, of agents). From a swarm
engineering perspective, they may not provide enough confidence in algorithm
correctness, since emergent behaviors arise only at larger scales. Furthermore,
studies at small scales may suffer from unmanifested theoretical and empirical
scalability issues.
